OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Associative Evidential Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

Evidential reasoning is cast as the problem of simplifying the evidence-hypothesis relation and constructing combination formulas that possess certain testable properties. Important classes of evidence as identifiers, annihilators, and idempotents and their roles in determining binary operations on intervals of reals are discussed. The appropriate way of constructing formulas for combining evidence and their limitations, for instance, in robustness, are presented.
Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
Venue: arXiv cs.AI 2013
Year: 2013

The multiple extension problem arises frequently in diagnostic and default inference. That is, we can often use any of a number of sets of defaults or possible hypotheses to explain observations or make Predictions. In default inference, some extensions seem to be simply wrong and we use qualitative techniques to weed out the unwanted ones. In the area of diagnosis, however, the multiple explanations may all seem reasonable, however improbable. Choosing among them is a matter of quantitative preference. Quantitative preference works well in diagnosis when knowledge is modelled causally. Here we suggest a framework that combines probabilities and defaults in a single unified framework that retains the semantics of diagnosis as construction of explanations from a fixed set of possible hypotheses. We can then compute probabilities incrementally as we construct explanations. Here we describe a branch and bound algorithm that maintains a set of all partial explanations while exploring a most promising one first. A most probable explanation is found first if explanations are partially ordered.
Steps Towards Programs that Manage Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

Reasoning under uncertainty in Al hats come to mean assessing the credibility of hypotheses inferred from evidence. But techniques for assessing credibility do not tell a problem solver what to do when it is uncertain. This is the focus of our current research. We have developed a medical expert system called MUM, for Managing Uncertainty in Medicine, that plans diagnostic sequences of questions, tests, and treatments. This paper describes the kinds of problems that MUM was designed to solve and gives a brief description of its architecture. More recently, we have built an empty version of MUM called MU, and used it to reimplement MUM and a small diagnostic system for plant pathology. The latter part of the paper describes the features of MU that make it appropriate for building expert systems that manage uncertainty.
Explanation of Probabilistic Inference for Decision Support Systems
Venue: arXiv cs.AI 2013
Year: 2013

An automated explanation facility for Bayesian conditioning aimed at improving user acceptance of probability-based decision support systems has been developed. The domain-independent facility is based on an information processing perspective on reasoning about conditional evidence that accounts both for biased and normative inferences. Experimental results indicate that the facility is both acceptable to naive users and effective in improving understanding.
Guidelines to the Problem of Location Management and Database Architecture for the Next Generation Mobile Networks
Venue: arXiv cs.NI 2013
Year: 2013

In near future, anticipated large number of mobile users may introduce very large centralized databases and increase end-to-end delays in location registration and call delivery on HLR-VLR database and will become infeasible. After observing several problems we propose some guidelines. Multitree distributed database, high throughput index structure, memory oriented database organization are used. Location management guidelines for moving user in overlapping network, neighbor discovery protocol (NDP), and global roaming rule are adopted. Analytic model and examples are presented to evaluate the efficiency of proposed guidelines.
Glyph Sorting: Interactive Visualization for Multi-dimensional Data
Venue: arXiv cs.GR 2013
Year: 2013

Glyph-based visualization is an effective tool for depicting multivariate information. Since sorting is one of the most common analytical tasks performed on individual attributes of a multi-dimensional data set, this motivates the hypothesis that introducing glyph sorting would significantly enhance the usability of glyph-based visualization. In this paper, we present a glyph-based conceptual framework as part of a visualization process for interactive sorting of multivariate data. We examine several technical aspects of glyph sorting and provide design principles for developing effective, visually sortable glyphs. Glyphs that are visually sortable provide two key benefits: 1) performing comparative analysis of multiple attributes between glyphs and 2) to support multi-dimensional visual search. We describe a system that incorporates focus and context glyphs to control sorting in a visually intuitive manner and for viewing sorted results in an Interactive, Multi-dimensional Glyph (IMG) plot that enables users to perform high-dimensional sorting, analyse and examine data trends in detail. To demonstrate the usability of glyph sorting, we present a case study in rugby event analysis for comparing and analysing trends within matches. This work is undertaken in conjunction with a national rugby team. From using glyph sorting, analysts have reported the discovery of new insight beyond traditional match analysis.
Flexible Interpretations: A Computational Model for Dynamic Uncertainty Assessment
Venue: arXiv cs.AI 2013
Year: 2013

The investigations reported in this paper center on the process of dynamic uncertainty assessment during interpretation tasks in real domain. In particular, we are interested here in the nature of the control structure of computer programs that can support multiple interpretation and smooth transitions between them, in real time. Each step of the processing involves the interpretation of one input item and the appropriate re-establishment of the system's confidence of the correctness of its interpretation(s).
An Explanation Mechanism for Bayesian Inferencing Systems
Venue: arXiv cs.AI 2013
Year: 2013

Explanation facilities are a particularly important feature of expert system frameworks. It is an area in which traditional rule-based expert system frameworks have had mixed results. While explanations about control are well handled, facilities are needed for generating better explanations concerning knowledge base content. This paper approaches the explanation problem by examining the effect an event has on a variable of interest within a symmetric Bayesian inferencing system. We argue that any effect measure operating in this context must satisfy certain properties. Such a measure is proposed. It forms the basis for an explanation facility which allows the user of the Generalized Bayesian Inferencing System to question the meaning of the knowledge base. That facility is described in detail.
Generalizing Fuzzy Logic Probabilistic Inferences
Venue: arXiv cs.AI 2013
Year: 2013

Linear representations for a subclass of boolean symmetric functions selected by a parity condition are shown to constitute a generalization of the linear constraints on probabilities introduced by Boole. These linear constraints are necessary to compute probabilities of events with relations between the. arbitrarily specified with propositional calculus boolean formulas.
Evaluation of Uncertain Inference Models I: PROSPECTOR
Venue: arXiv cs.AI 2013
Year: 2013

This paper examines the accuracy of the PROSPECTOR model for uncertain reasoning. PROSPECTOR's solutions for a large number of computer-generated inference networks were compared to those obtained from probability theory and minimum cross-entropy calculations. PROSPECTOR's answers were generally accurate for a restricted subset of problems that are consistent with its assumptions. However, even within this subset, we identified conditions under which PROSPECTOR's performance deteriorates.
On Implementing Usual Values
Venue: arXiv cs.AI 2013
Year: 2013

In many cases commonsense knowledge consists of knowledge of what is usual. In this paper we develop a system for reasoning with usual information. This system is based upon the fact that these pieces of commonsense information involve both a probabilistic aspect and a granular aspect. We implement this system with the aid of possibility-probability granules.
On the Combinality of Evidence in the Dempster-Shafer Theory
Venue: arXiv cs.AI 2013
Year: 2013

In the current versions of the Dempster-Shafer theory, the only essential restriction on the validity of the rule of combination is that the sources of evidence must be statistically independent. Under this assumption, it is permissible to apply the Dempster-Shafer rule to two or mere distinct probability distributions.
GUI Database for the Equipment Store of the Department of Geomatic Engineering, KNUST
Venue: arXiv cs.DB 2013
Year: 2013

The geospatial analyst is required to apply art, science, and technology to measure relative positions of natural and man-made features above or beneath the earths surface, and to present this information either graphically or numerically. The reference positions for these measurements need to be well archived and managed to effectively sustain the activities in the spatial industry. The research herein described highlights the need for an information system for the Land Surveyors Equipment Store. Such a system is a database management system with a user friendly graphical interface. This paper describes one such system that has been developed for the Equipment Store of the Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology, Ghana. The system facilitates efficient management and location of instruments, as well as easy location of beacons together with their attribute information, it provides multimedia information about instruments in an Equipment Store. Digital camera was used capture the pictorial descriptions of the beacons. Geographic Information System software was employed to visualize the spatial location of beacons and to publish the various layers for the Graphical User Interface. The aesthetics of the interface was developed with user interface design tools and coded by programming. The developed Suite, powered by a reliable and fully scalable database, provides an efficient way of booking and analyzing transactions in an Equipment Store.
The Complexity of Approximating Vertex Expansion
Venue: arXiv cs.CC 2013
Year: 2013

We study the complexity of approximating the vertex expansion of graphs $G = (V,E)$, defined as \[ Φ^V := \min_{S \subset V} n \cdot \frac{|N(S)|}{|S| |V \backslash S|}. \] We give a simple polynomial-time algorithm for finding a subset with vertex expansion $O(\sqrt{OPT \log d})$ where $d$ is the maximum degree of the graph. Our main result is an asymptotically matching lower bound: under the Small Set Expansion (SSE) hypothesis, it is hard to find a subset with expansion less than $C\sqrt{OPT \log d}$ for an absolute constant $C$. In particular, this implies for all constant $ε> 0$, it is SSE-hard to distinguish whether the vertex expansion $< ε$ or at least an absolute constant. The analogous threshold for edge expansion is $\sqrt{OPT}$ with no dependence on the degree; thus our results suggest that vertex expansion is harder to approximate than edge expansion. In particular, while Cheeger's algorithm can certify constant edge expansion, it is SSE-hard to certify constant vertex expansion in graphs. Our proof is via a reduction from the {\it Unique Games} instance obtained from the \SSE hypothesis to the vertex expansion problem. It involves the definition of a smoother intermediate problem we call {\sf Analytic Vertex Expansion} which is representative of both the vertex expansion and the conductance of the graph. Both reductions (from the UGC instance to this problem and from this problem to vertex expansion) use novel proof ideas.
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Venue: arXiv cs.CV 2013
Year: 2013

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: Local Reference Frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and 96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets.
RSA Signature: Behind the Scenes
Venue: arXiv cs.CR 2013
Year: 2013

In this paper, we present a complete digital signature message stream, just the way the RSA digital signature scheme does it. We will focus on the operations with large numbers due to the fact that operating with large numbers is the essence of RSA that cannot be understood by the usual illustrative examples with small numbers.
Towards a better approximation for sparsest cut?
Venue: arXiv cs.DS 2013
Year: 2013

We give a new $(1+ε)$-approximation for sparsest cut problem on graphs where small sets expand significantly more than the sparsest cut (sets of size $n/r$ expand by a factor $\sqrt{\log n\log r}$ bigger, for some small $r$; this condition holds for many natural graph families). We give two different algorithms. One involves Guruswami-Sinop rounding on the level-$r$ Lasserre relaxation. The other is combinatorial and involves a new notion called {\em Small Set Expander Flows} (inspired by the {\em expander flows} of ARV) which we show exists in the input graph. Both algorithms run in time $2^{O(r)} \mathrm{poly}(n)$. We also show similar approximation algorithms in graphs with genus $g$ with an analogous local expansion condition. This is the first algorithm we know of that achieves $(1+ε)$-approximation on such general family of graphs.
Generic Behaviour Similarity Measures for Evolutionary Swarm Robotics
Venue: arXiv cs.NE 2013
Year: 2013

Novelty search has shown to be a promising approach for the evolution of controllers for swarm robotics. In existing studies, however, the experimenter had to craft a domain dependent behaviour similarity measure to use novelty search in swarm robotics applications. The reliance on hand-crafted similarity measures places an additional burden to the experimenter and introduces a bias in the evolutionary process. In this paper, we propose and compare two task-independent, generic behaviour similarity measures: combined state count and sampled average state. The proposed measures use the values of sensors and effectors recorded for each individual robot of the swarm. The characterisation of the group-level behaviour is then obtained by combining the sensor-effector values from all the robots. We evaluate the proposed measures in an aggregation task and in a resource sharing task. We show that the generic measures match the performance of domain dependent measures in terms of solution quality. Our results indicate that the proposed generic measures operate as effective behaviour similarity measures, and that it is possible to leverage the benefits of novelty search without having to craft domain specific similarity measures.
Relative Entropy, Probabilistic Inference and AI
Venue: arXiv cs.AI 2013
Year: 2013

Various properties of relative entropy have led to its widespread use in information theory. These properties suggest that relative entropy has a role to play in systems that attempt to perform inference in terms of probability distributions. In this paper, I will review some basic properties of relative entropy as well as its role in probabilistic inference. I will also mention briefly a few existing and potential applications of relative entropy to so-called artificial intelligence (AI).
Exact Reasoning Under Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

This paper focuses on designing expert systems to support decision making in complex, uncertain environments. In this context, our research indicates that strictly probabilistic representations, which enable the use of decision-theoretic reasoning, are highly preferable to recently proposed alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore, we discuss the language of influence diagrams and a corresponding methodology -decision analysis -- that allows decision theory to be used effectively and efficiently as a decision-making aid. Finally, we use RACHEL, a system that helps infertile couples select medical treatments, to illustrate the methodology of decision analysis as basis for expert decision systems.
Approximate optimal cooperative decentralized control for consensus in a topological network of agents with uncertain nonlinear dynamics
Venue: arXiv cs.SY 2013
Year: 2013

Efforts in this paper seek to combine graph theory with adaptive dynamic programming (ADP) as a reinforcement learning (RL) framework to determine forward-in-time, real-time, approximate optimal controllers for distributed multi-agent systems with uncertain nonlinear dynamics. A decentralized continuous time-varying control strategy is proposed, using only local communication feedback from two-hop neighbors on a communication topology that has a spanning tree. An actor-critic-identifier architecture is proposed that employs a nonlinear state derivative estimator to estimate the unknown dynamics online and uses the estimate thus obtained for value function approximation.
Formal Verification of a C Value Analysis Based on Abstract Interpretation
Venue: arXiv cs.PL 2013
Year: 2013

Static analyzers based on abstract interpretation are complex pieces of software implementing delicate algorithms. Even if static analysis techniques are well understood, their implementation on real languages is still error-prone. This paper presents a formal verification using the Coq proof assistant: a formalization of a value analysis (based on abstract interpretation), and a soundness proof of the value analysis. The formalization relies on generic interfaces. The mechanized proof is facilitated by a translation validation of a Bourdoncle fixpoint iterator. The work has been integrated into the CompCert verified C-compiler. Our verified analysis directly operates over an intermediate language of the compiler having the same expressiveness as C. The automatic extraction of our value analysis into OCaml yields a program with competitive results, obtained from experiments on a number of benchmarks and comparisons with the Frama-C tool.
Modified Soft Brood Crossover in Genetic Programming
Venue: arXiv cs.NE 2013
Year: 2013

Premature convergence is one of the important issues while using Genetic Programming for data modeling. It can be avoided by improving population diversity. Intelligent genetic operators can help to improve the population diversity. Crossover is an important operator in Genetic Programming. So, we have analyzed number of intelligent crossover operators and proposed an algorithm with the modification of soft brood crossover operator. It will help to improve the population diversity and reduce the premature convergence. We have performed experiments on three different symbolic regression problems. Then we made the performance comparison of our proposed crossover (Modified Soft Brood Crossover) with the existing soft brood crossover and subtree crossover operators.
Aloha Games with Spatial Reuse
Venue: arXiv cs.GT 2013
Year: 2013

Aloha games study the transmission probabilities of a group of non-cooperative users which share a channel to transmit via the slotted Aloha protocol. This paper extends the Aloha games to spatial reuse scenarios, and studies the system equilibrium and performance. Specifically, fixed point theory and order theory are used to prove the existence of a least fixed point as the unique Nash equilibrium (NE) of the game and the optimal choice of all players. The Krasovskii's method is used to construct a Lyapunov function and obtain the conditions to examine the stability of the NE. Simulations show that the theories derived are applicable to large-scale distributed systems of complicated network topologies. An empirical relationship between the network connectivity and the achievable total throughput is finally obtained through simulations.
On topological changes in the Delaunay triangulation of moving points
Venue: arXiv cs.CG 2013
Year: 2013

Let $P$ be a collection of $n$ points moving along pseudo-algebraic trajectories in the plane. One of the hardest open problems in combinatorial and computational geometry is to obtain a nearly quadratic upper bound, or at least a subcubic bound, on the maximum number of discrete changes that the Delaunay triangulation $\DT(P)$ of $P$ experiences during the motion of the points of $P$. In this paper we obtain an upper bound of $O(n^{2+\eps})$, for any $\eps>0$, under the assumptions that (i) any four points can be co-circular at most twice, and (ii) either no triple of points can be collinear more than twice, or no ordered triple of points can be collinear more than once.
Towards more accurate clustering method by using dynamic time warping
Venue: arXiv cs.LG 2013
Year: 2013

An intrinsic problem of classifiers based on machine learning (ML) methods is that their learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still possible to complete the machine learning tasks in reasonable time. In this context, we present in this paper a more accurate simple process to speed up ML methods. An unsupervised clustering algorithm is combined with Expectation, Maximization (EM) algorithm to develop an efficient Hidden Markov Model (HMM) training. The idea of the proposed process consists of two steps. In the first step, training instances with similar inputs are clustered and a weight factor which represents the frequency of these instances is assigned to each representative cluster. Dynamic Time Warping technique is used as a dissimilarity function to cluster similar examples. In the second step, all formulas in the classical HMM training algorithm (EM) associated with the number of training instances are modified to include the weight factor in appropriate terms. This process significantly accelerates HMM training while maintaining the same initial, transition and emission probabilities matrixes as those obtained with the classical HMM training algorithm. Accordingly, the classification accuracy is preserved. Depending on the size of the training set, speedups of up to 2200 times is possible when the size is about 100.000 instances. The proposed approach is not limited to training HMMs, but it can be employed for a large variety of MLs methods.